Readers will progress from building a basic physics engine to creating intelligent moving objects and complex systems, setting the foundation for further experiments in generative design. Subjects covered include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. The book's examples are written in Processing, an open-source language and development environment built on top of the Java programming language. On the book's website (http://www.natureofcode.com), the examples run in the browser via Processing's JavaScript mode.

    The Mikado Method is a process for surfacing the dependencies in a codebase, so that you can systematically eliminate technical debt and get things done.It gets its name from a simple game commonly known as "pick-up sticks." You start with a jumbled pile of sticks. The goal is to remove the Mikado, or Emperor, stick without disturbing the others. Players carefully remove sticks one at a time, leaving the rest of the heap intact, slowly exposing the Mikado. The game is a great metaphor for eliminating technical debt—carefully extracting each intertwined dependency until you're able to successfully resolve the central issue and move on.The Mikado Method is a book by the creators of this process. It describes a pragmatic, straightforward, and empirical method to plan and perform non-trivial technical improvements on an existing software system. The method has simple rules, but the applicability is vast. As you read, you'll practice a step-by-step system for identifying the scope and nature of your technical debt, mapping the key dependencies, and determining the safest way to approach the "Mikado"-your goal. A natural byproduct of this process is the Mikado Graph, a minimalistic, relevant, just-in-time roadmap and information radiator that reflects deep understanding of how your system works.

    These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of Web intelligence, as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected.Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

    Learn You a Haskell for Great Good! introduces programmers familiar with imperative languages (such as C++, Java, or Python) to the unique aspects of functional programming. Packed with jokes, pop culture references, and the author's own hilarious artwork, Learn You a Haskell for Great Good! eases the learning curve of this complex language, and is a perfect starting point for any programmer looking to expand his or her horizons. The well-known web tutorial on which this book is based is widely regarded as the best way for beginners to learn Haskell, and receives over 30,000 unique visitors monthly.

    A classic title on the PC revolution originally published in 1986. Featuring Bill Gates, Andy Hertzfeld, Charles Simonyi, Ray Ozzie, Michael Hawley and many more.

    Based on two popular talks from author Lea Verou--including "CSS3 Secrets: 10 things you may not know about CSS"--this practical guide provides intermediate to advanced CSS developers with more than 40 undocumented techniques and tips for using CSS3 to create better websites.The talks that spawned this book have been top-rated by attendees in every conference they were presented, and praised in industry media such as ."net" magazine.Get information you won't find in any other bookLearn through small, easily digestible chaptersHelps you understand CSS more deeply so you can improve your own solutionsApply Lea's techniques to practically every CSS problem you faceGain tips from a rockstar author who serves as an Invited Expert in W3C's CSS Working Group

    This is especially true when it comes to object-oriented and template programming. The C++ Pocket Reference is a memory aid for C++ programmers, enabling them to quickly look up usage and syntax for unfamiliar and infrequently used aspects of the language. The book's small size makes it easy to carry about, ensuring that it will always be at-hand when needed. Programmers will also appreciate the book's brevity; as much information as possible has been crammed into its small pages.In the C++ Pocket Reference, you will find:Information on C++ types and type conversions Syntax for C++ statements and preprocessor directives Help declaring and defining classes, and managing inheritance Information on declarations, storage classes, arrays, pointers, strings, and expressions Refreshers on key concepts of C++ such as namespaces and scope More!C++ Pocket Reference is useful to Java and C programmers making the transition to C++, or who find themselves occasionally programming in C++. The three languages are often confusingly similar. This book enables programmers familiar with C or Java to quickly come up to speed on how a particular construct or concept is implemented in C++.Together with its companion STL Pocket Reference, the C++ Pocket Reference forms one of the most concise, easily-carried, quick-references to the C++ language available.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.This book examines:Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable log structured storage engines, with differences and use-cases for eachDistributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns, from UDP to reliable consensus protocolsDatabase clusters: Discover how to achieve consistent models for replicated data

    He includes information on Java Platform Standard Edition 6 (Java SE 6) and offers complete coverage of the Java language, its syntax, keywords, and fundamental programming principles.

    

    That's the philosophy behind ethical hacking, and it's a growing field. Prepare for certification in this important area with this advanced study guide that covers all exam objectives for the challenging CEH Certified Ethical Hackers exam. The book provides full coverage of exam topics, real-world examples, and a CD with additional materials for extra review and practice. Covers ethics and legal issues, footprinting, scanning, enumeration, system hacking, trojans and backdoors, sniffers, denial of service, social engineering, session hijacking, hacking Web servers, Web application vulnerabilities, and more Walks you through exam topics and includes plenty of real-world scenarios to help reinforce concepts Includes a CD with review questions, bonus exams, and more study tools This is the ideal guide to prepare you for the new CEH certification exam. Reviews

    Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.

    Beginning with an introduction to array formulas, this manual examines topics such as how they differ from ordinary formulas, the benefits and drawbacks of their use, functions that can and cannot handle array calculations, and array constants and functions. Among the practical applications surveyed include how to extract data from tables and unique lists, how to get results that match any criteria, and how to utilize various methods for unique counts. This book contains 529 screen shots.